Indoor positioning requires novel systems and solutions that are specifically developed and deployed for this purpose. The “traditional” positioning technologies, which are mainly used outdoors, for instance satellite and cellular positioning technologies, cannot deliver such performance indoors that would enable seamless and equal navigation experience in both environments.
The required positioning accuracy (within 2 to 3 meters), coverage (˜100%) and floor detection are challenging to achieve with satisfactory performance levels with the systems and signals that were not designed and specified for the indoor use cases in the first place. Satellite-based radio navigation signals simply do not penetrate through the walls and roofs for the adequate signal reception and the cellular signals have too narrow bandwidth for accurate ranging by default.
Several indoor-dedicated solutions have already been developed and commercially deployed during the past years, for instance solutions based on pseudolites (GPS-like short-range beacons), ultra-sound positioning, BTLE signals (e.g. High-Accuracy Indoor Positioning, HAIP) and Wi-Fi fingerprinting. What is typical to these solutions is that they require either deployment of totally new infrastructure (beacons, tags to name but a few non-limiting examples) or manual exhaustive radio surveying of the buildings including all the floors, spaces and rooms. This is rather expensive and will take a considerable amount of time to build the coverage to the commercially expected level, which in some cases narrowed the potential market segment only to very thin customer base, for instance for health care or dedicated enterprise solutions. Also, the diversity of these technologies makes it difficult to build a globally scalable indoor positioning solution, and the integration and testing will become complex if a large number of technologies needs to be supported in the consumer devices, such as smartphones.
For an indoor positioning solution to be commercially successful, that is, i) being globally scalable, ii) having low maintenance and deployment costs, and iii) offering acceptable end-user experience, the solution needs to be based on an existing infrastructure in the buildings and on existing capabilities in the consumer devices. This leads to an evident conclusion that the indoor positioning needs to be based on Wi-Fi- and/or Bluetooth (BT)-technologies that are already supported in every smartphone, tablet, laptop and even in the majority of feature phones. It is, thus, required to find a solution that uses the Wi-Fi- and BT-radio signals in such a way that makes it possible to achieve 2 to 3 meter horizontal positioning accuracy, close to 100% floor detection with the ability to quickly build the global coverage for this approach.
Further, a novel approach for radio-based indoor positioning that models for instance the Wi-Fi-radio environment (or any similar radio e.g. Bluetooth) from observed Received Signal Strength (RSS)-measurements as two-dimensional radiomaps and is hereby able to capture the dynamics of the indoor radio propagation environment in a compressable and highly accurate way. This makes it possible to achieve unprecedented horizontal positioning accuracy with the Wi-Fi signals only within the coverage of the created radiomaps and also gives highly reliable floor detection.
To setup indoor positing in a building, the radio environment in the building needs to be surveyed. This phase is called “radiomapping”. In the radiomapping phase samples containing geolocation (like latitude, longitude, altitude; or x, y, floor) and radio measurements (Wi-Fi and/or Bluetooth radio node identities and signal strengths). Having these samples allows understanding how the radio signals behave in the building. This understanding is called a “radiomap”. The radiomap enables localization capability to devices when they observe varying radio signals, the signals can be compared to the radiomap resulting in the location information.
The radio samples for the radiomap may be collected with separate tools or crowd-sourced from the user devices. While automated crowd-sourcing can enable indoor localization in large amount of buildings, manual data collection using special tools may be the best option, when the highest accuracy is desired.